LdDMDenoising

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Low-dose Digital Mammography with Deep Learning

LdDMDenoising 최신버젼 다운로드

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# Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

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This repository contains the training and testing codes for the paper "[Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography](https://doi.org/10.1016/j.artmed.2023.102555)". For simulating dose reduction on clinical images, we used the codes available [here](https://lucasbusp.wixsite.com/lucasborges/simulation-of-dose-reduction). Also, we used a model-based (MB) restoration as a benchmark, also available [here](https://lucasbusp.wixsite.com/lucasborges/image-restoration), which uses the commonly known [BM3D](https://webpages.tuni.fi/foi/GCF-BM3D/).

## Reference:

@article{shan2023impact,
      title={Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography},
      author={Shan, Hongming and Vimieiro, Rodrigo B and Borges, Lucas R and Vieira, Marcelo AC and Wang, Ge},
      journal={Artificial Intelligence in Medicine},
      volume={142},
      pages={102555},
      year={2023},
      publisher={Elsevier}
    }
---
AI-based X-ray Imaging System ([AXIS](https://wang-axis.github.io))  
Department of Biomedical Engineering  
Rensselaer Polytechnic Institute  
Troy - USA

Laboratory of Computer Vision ([Lavi](http://iris.sel.eesc.usp.br/lavi/))  
Department of Electrical and Computer Engineering  
São Carlos School of Engineering, University of São Paulo  
São Carlos - Brazil

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